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Introduction

When industrial equipment faces unexpected glitches, the loss goes beyond the cost of replacing the item, owing to the forced and unexpected downtimes. Even a minor breakdown can halt production and delay orders. This can have serious financial implications. According to a GE digital study, 82% of manufacturing companies face unexpected downtimes.

Digital twins can play a significant role in avoiding such equipment shutdowns and minimizing the cost of running operations in industrial manufacturing by predicting failures well in advance.

This blog will include a step-by-step guide to implementing digital twins to prevent downtimes by deploying a predictive maintenance solution..

Advantages of Digital Twins in Predictive Maintenance

One of the key advantages of digital twins is it can help industries usher in the era of intelligent manufacturing by being proactive instead of reactive. Industries can act just in time to address any product performance issues in advance.

Digital twins can help visualize precisely when and where maintenance is required and at what time, allowing production, operations and maintenance managers to take steps proactively.

For example, a manufacturer may use a digital twin to monitor the performance of a large-scale production line. Digital twins can identify potential issues with individual components, such as motors or conveyors, before they cause a shutdown. This allows for proactive maintenance and repairs, reducing the risk of unexpected downtime.

Therefore, the cost and time savings can be substantial by minimizing equipment downtime and timely repairs.

Further, implementing digital twin solutions can provide actionable insights into asset behavior and performance, optimizing asset utilization. Detecting potential issues in advance: Equipment lifespan can be significantly enhanced by seeing potential issues and performing maintenance at the right time.

Implementing Digital Twins

Here is the step-by-step guide for implementing digital twins.

1. Define Goals

Defining specific goals is essential to implement digital twin technology successfully. By setting clear and measurable goals, businesses can effectively leverage digital twin technology to optimize their equipment performance and avoid costly downtime.

Let’s consider two examples:

  • Cooling operations are critical in a nuclear power plant, and pumps supplying water or coolant are paramount. A specific goal for implementing digital twin technology in this scenario could be to achieve 100% uptime of the cooling pump by monitoring it to ensure no mechanical or electrical breakdowns. Here, the sole objective of implementing DT is to maintain uptime.
  • In specific pharma industrial manufacturing units, maintaining temperature and humidity-controlled environments can be a critical goal. Likewise, there can be multiple goals for various equipment in an industrial setting.

Defining goals is essential as it impacts platform selection,  budget and overall ROI. Whether you are looking for a faceless digital twin, 3D digital twin or an immersive digital twin will depend on your objectives.

2. Identify Critical

Once you identify the specific goals you want to achieve with digital twins, locate elements impacting these goals. Now, study the factors or elements that affect each piece of equipment or stream of other minor equipment associated with this equipment. For example, everything needs to be monitored if there is a water pump, from the electrical motor, water floor, water pressure to the actual pump.

So, if you want to create a digital twin of a pump, identify the data points impacting the electric motor, which can be anything like winding temperature to current flow. However, you also need to consider the water pump. The factors affecting the water pump can range from the remaining life of a bearing, RPM, pump rotation speed, an inlet and outlet pressure, and many other controlling parameters.

3. Build Behavior-Based Models

In this stage, we build a behavioral model of the assets based on the available data. Here, we must understand two concepts: asset digital twins and product digital twins. They depict the behavior of the assets in the digital environment.

Asset and product twins utilize condition-based monitoring data to identify anomalies, forecast equipment failures, and plan maintenance schedules for physical assets.

Therefore, we can build two models: one depicting the current condition and another a predictive model, which can predict the need for replacing a specific component or timely maintenance.

The advantage of the predictive model is it can be leveraged for predictive purposes, such as anticipating an electrical motor failure several months in advance based on its current operating condition.

With digital twins, you can simulate one or multiple aspects of your equipment – be it behavioural or functional or electrical.

4. Deploy Digital Twin Models

This process involves bridging the gap between the digital and operational worlds. The process includes integrating models with accurate world data and systems for start monitoring and analysis.

This is done via a platform to capture data by installing sensors and configuring the In digital twins; the model deployment process is a crucial step involving transferring the machine learning models developed in the development phase to the operational phase.

The deployment process involves integrating the models with real-world data and systems for real-time monitoring and analysis. This also requires configuring the digital twin to simulate the equipment’s behavior.

Once the model is deployed in real-time, it can be used for various purposes, such as predicting the behavior of the physical system, optimizing its performance, or providing insights for decision-making.

5. Configure And Monitor Data

While capturing real-time data, it is essential to ensure data quality. The data should be arrested and monitored 24*7. There should not be instances of missing data. Additionally, there should be some report or dashboard that gives condition-based monitoring output to stakeholders. This gives them a clear idea of the health and performance of the equipment, whether it is green, red, or neutral.

Data is also required to define thresholds below or above which your operations and maintenance team must get alerts and notifications via sms/email for their attention. Timely alerts and notifications help in taking proactive measures that can help prevent unexpected downtime.

6. Continuously Optimize Data

Once the digital twin model starts getting real-time data, the focus shifts to improving the accuracy and reliability of the model. This can be done by finetuning the model’s model or adding new data to the training dataset.

For instance, let’s take the above pump example.

The model’s level may be a high false-positive rate; once the new data, such as temperature, pressure, and vibration, is added to the model parameters, the model can be retrained to improve its accuracy and minimize the false-positive rate.

This way, the digital twin model keeps learning and improves with time.

Though we have taken an example of a simple pump, digital twin technology can have more sophisticated applications in industries such as energy & power utilities, healthcare, supply chain management, automotive, and pharma.

Closing Thoughts

In summary, understanding what is happening in the industrial setup at the most granular level has been a dream for manufacturers for a long time. Digital twins have turned this dream into reality. This can help reduce the unexpected instances of equipment failure. Implementing digital twin technology solutions can help in proactively managing equipment performance, identifying the causes of failure,  and utilizing the resources prudently.

The true success of digital twins lies in creating a genuine shadow of assets and products into powerful predictive tools.

By following the digital twins implementation process outlined above, one can set the foundation for a robust IT-OT capability required for digital twin technology implementation.

To know more about how Digital twins can be implemented in your industry, please contact us or do reach out to me.

Nitin
Nitin Tappe

After successful stint in a corporate role, Nitin is back to what he enjoys most – conceptualizing new software solutions to solve business problems. Nitin is a postgraduate from IIT, Mumbai, India and in his 24 years of career, has played key roles in building a desktop as well as enterprise solutions right from idealization to launch which are adopted by many Fortune 500 companies. As a Founder member of Pratiti Technologies, he is committed to applying his management learning as well as the passion for building new solutions to realize your innovation with certainty.

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